TREND: United States Census Bureau. International Trade Datasets: Exports by End-Use Code | Country: Dominican Republic, Mexico, Singapore, Thailand, United Kingdom | Indicator: Total Value | Code: 00370, 2013 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 001-067-003
Bibliographic record
Abstract
United States Census Bureau. International Trade Datasets: Exports by End-Use Code | Country: Dominican Republic, Mexico, Singapore, Thailand, United Kingdom | Indicator: Total Value | Code: 00370, 2013 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 001-067-003 Dataset: Provides statistics on US exports using the end-use classification system. This classification system is based on principal use rather than the physical characteristics of the merchandise. End-use codes are assigned by the Bureau of Economic Analysis under the US Department of Commerce. The broad end-use categories used by BEA and the Census Bureau are foods, feeds, and beverages; industrial supplies and materials; capital goods except automotive; automotive vehicles, parts, and engines; consumer goods except food and automotive; and “other” goods. The end-use classification system includes about 200 subcategories of exports and 200 subcategories of imports. The Monthly and Annual International Trade Datasets provide detailed data on trade between the United States and its international trade partners. The ITD provide the most comprehensive current month and cumulative year-to-date export and import statistics using multiple commodity classification systems. Previously released trade data are revised annually with the publication of April statistics. Data on US exports of merchandise from the US to all countries, except Canada, are compiled from the Electronic Export Information (EEI) filed by the US Principal Party of Interest or their agents through the US Customs' Automated Commercial Environment (ACE). Filing the EEI is mandatory under Chapter 9, Title 13, United States Code. Qualified exporters, or their agents, submit EEI data by automated means directly to the US Census Bureau. Data on US imports of merchandise are compiled primarily from automated data submitted through the US Customs' Automated Commercial Environment (ACE). Other sources of import data include import entry summary forms, warehouse withdrawal forms, and Foreign Trade Zone documents. Data on imports of electricity and natural gas from Canada are obtained from Canadian sources. Statistics are available by district (or port) of exportation (for exports) or entry (for imports) and by trading partner. https://www.census.gov/data/developers/data-sets/international-trade.html Category: International Relations and Trade Subject: International Trade, Exports Source: United States Census Bureau The United States Census Bureau is a bureau of the US Department of Commerce. The major functions of the Census Bureau are authorized by Article 2, Section 2 of the United States Constitution, which provides that a census of population shall be taken every 10 years, and by Title 13 and Title 26 of the United States Code of Federal Regulations. The Census Bureau is responsible for numerous statistical programs, including census and surveys of households, governments, manufacturing and industries, and for US foreign trade statistics. The first US census was conducted in 1790 for the purposes of apportioning state representation in the US House of Representatives and for the apportionment of taxes. https://www.census.gov
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.084 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".